The present embodiments relate to boundary detection. In particular, a closed boundary is detected in a medical image.
Segmentation of a vessel cross-sectional boundary is used for diagnosis. The area of the vessel may be determined for analysis of a possible stenosis. Vascular research may benefit from segmentation to highlight the location of the vessel or other elliptical structure in a patient. The segmentation can be also used together with functional imaging, such as phase contrast MRI for advanced vascular quantification. Reproducibility of the measurements is important in order to achieve low intra and inter-observer variability for the same clinical data.
One technique with sub-voxel accuracy and efficient running time for the cross-sectional segmentation of vessels is based on a minimum mean cycle optimization method. As disclosed in U.S. Published patent application No. 2007/0248250, the disclosure of which is incorporated herein by reference, a cyclic graph using multi-scale mean-shift edge responses is used to iteratively locate the boundary in an image. The method relies on an input of a single seed point. Other semi-automatic segmentation may also use one or more input seed points.
Semi-automatic vessel cross-sectional segmentation algorithms may be sensitive to the location of the input seed point. Where the spatial domain and/or cost function computations are seed dependent, the segmentation may vary depending on the starting location of the seed. FIGS. 1 and 2 show a same medical image 30. The location of the seed point 32 is different in each medical image. Using the semi-automatic method of U.S. Published patent application No. 2007/0248250 on the two-dimensional image 30 with the different starting location of the seed points 32 results in identification of different boundaries 34. The boundary detection is sensitive to the location of the seed point 32 because of the construction of the cyclic graph around the seed point 32. Other semi-automatic segmentation may be sensitive to the placement of seed points. This sensitivity results in less reproducible results from the segmentation.